Statistical Modelling of Cell Cycle Dynamics
Sara Larsson
Centre for Mathematical Sciences
Mathematical Statistics
Lund University
2007
ISBN 9789162871192
LUNFMS10202007

Abstract:


It is of great importance to increase the knowledge of various cell cycle
kinetic parameters and the objective of this thesis is to use stochastic
models to interpret Bromodeoxyuridine (BrdUrd) DNA Flow Cytometry (FCM) derived
data in order to estimate such parameters. The cell cycle is the process
of growth and division that is essential for an organism to increase in size.
The cell cycle consists of several consecutive phases and one of these is
the S phase, during which DNA is duplicated. The duration of this phase is
the main focus of the thesis although the following G2 phase is also studied.
Most of the previous methods developed to estimate the duration of the S
phase consider its length as a deterministic value. The variation within
a cell population is large though and to obtain information regarding the
variation in S phase duration it is necessary to consider stochastic models.


When using the BrdUrd DNA FCM method the DNA content can be measured under
certain circumstances and the DNA distribution of cells followed through
the cell cycle. Cells in S phase are labelled with BrdUrd and it is the DNA
content in this subpopulation of cells that is measured at various times
after BrdUrd labelling.


In the first place, the models considered in this thesis are based on asymptotic
results from branching processes. To obtain information regarding the duration
of the S phase it is crucial to have a model for the rate at which DNA is
duplicated. There is no parametric model known to describe this rate and
therefore nonparametric approaches are proposed. Furthermore, the duration
of the S phase is assumed to be gamma distributed, resulting in an expression
for the progression of the DNA distribution over time. The derived expression
is then compared with the obtained data. However, there is also a measurement
variation which has to be modelled. Different approaches are investigated;
a deconvolution approach and including the measurement variation in the
likelihood.


The estimated durations of the S phase and the G2 phase turn out to be rather
large, strengthening the importance of considering stochastic models when
modelling cell cycle dynamics.


Key words:

Branching processes, cell cycle kinetics, DNA distribution, S phase duration,
G2 phase duration, DNA replication, flow cytometry,








